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<div class="title">CL-CBS </div>  </div>
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<div class="textblock"><h2>Overview</h2>
<p><b>Car-Like Conflict Based Search (CL-CBS)</b> is an efficient and complete solver of Multi-Agent Path Finding for Car-like Robots problem. It applies a body conflict tree to address collisions considering the shape of agents. It also includes a new algorithm Spatiotemporal Hybrid-State A* as the single-agent path planner to generate path satisfying both kinematic and spatiotemporal constraints.</p>
<p>The source code are open source in <a href="https://github.com/APRIL-ZJU/CL-CBS">GitHub</a>. The video demonstration can be found on <a href="https://www.youtube.com/watch?v=KThsX04ABvc">YouTube</a>.</p>
<h2>Source Code</h2>
<h3>Requirement</h3>
<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;sudo apt-get install g++ cmake libboost-program-options-dev libyaml-cpp-dev clang-tidy clang-format python3-matplotlib libompl-dev libeigen3-dev</div></div><!-- fragment --> <h3>Build</h3>
<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;mkdir build </div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;cd build</div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;cmake -DCMAKE_BUILD_TYPE=Release  ..</div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;make -j8</div></div><!-- fragment --><ul>
<li><code>make</code>: Build CL-CBS code</li>
<li><code>make docs</code>: Build doxygen documentation</li>
<li><code>make clang-format</code>: Re-format all source files</li>
<li><code>make all</code>: Build all three targets above</li>
</ul>
<h3>Run example instances</h3>
<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;# make sure your are in build folder</div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;# default 10 agent in a batch</div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;./CL-CBS -i ../benchmark/map50by50/agents5/empty/map_50by50_obst0_agents5_ex11.yaml -o output.yaml </div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;# or compute 20 agents in a whole batch</div><div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;./CL-CBS -i ../benchmark/map100by100/agents20/obstacle/map_100by100_obst50_agents20_ex13.yaml -o output.yaml -b 20 </div></div><!-- fragment --><h3>Visualize Results</h3>
<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;# make sure your are in build folder</div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;python3 ../src/visualize.py -m  ../benchmark/map100by100/agents20/obstacle/map_100by100_obst50_agents20_ex13.yaml  -s output.yaml</div></div><!-- fragment --><h3>Agent Configuration</h3>
<p>The agent configurations, including the size, the kinematic constraints, and penalty functions can be changed in <code>src/config.yaml</code>.</p>
<h2>Benchmark</h2>
<p>Benchmark for evaluating CL-MAPF problem are available in <code>benchmark</code> folder. It contains 3000 unique instances with different map size and agents number.</p>
<p>The folder are arranged like follows, each mapset contains 60 instances:</p>
<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;benchmark</div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;├── map100by100</div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;│   ├── agents10</div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;│   │   ├── empty</div><div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;│   │   └── obstacle</div><div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;│   ...</div><div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;├── map300by300</div><div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;│   ├── agents10</div><div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;│   │   ├── empty</div><div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;│   │   └── obstacle</div><div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;│   ...</div><div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;└── map50by50</div><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;    ├── agents10</div><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;    │   ├── empty</div><div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;    │   └── obstacle</div><div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;    ...</div></div><!-- fragment --><p>The instance are in <code>yaml</code> format.</p>
<p>A typical result from benchmark acts like below:</p>
<h2>Credits</h2>
<p>This code was developed by the <a href="https://github.com/APRIL-ZJU">APRIL Lab</a> in Zhejiang University.</p>
<p>For researchers that have leveraged or compared to this work, please cite the following:</p>
<p>Licheng Wen, Zhen Zhang, Zhe Chen, Xiangrui Zhao, and Yong Liu. CL-MAPF: Multi-Agent Path Finding for Car-Like Robots with Kinematic and Spatiotemporal Constraints.[<a href="https://arxiv.org/abs/2011.00441">arxiv</a>]</p>
<h2>License</h2>
<p>The code is provided under the <a href="https://opensource.org/licenses/MIT">MIT License</a>. </p>
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